Informações sobre o curso: In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Desenvolvido por: Universidade de MinnesotaUniversidade de Minnesota
Ministrado por: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Ministrado por: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Informações básicas | Curso 2 de 5 no Recommender Systems Specialization |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
Programa
SEMANA 1
Preface
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
1 vídeo, 1 leitura
- Vídeo: Course Introduction
- 阅读: Course Structure Outline
User-User Collaborative Filtering Recommenders Part 1
5 vídeos
- Vídeo: User-User Collaborative Filtering
- Vídeo: Configuring User-User Collaborative Filtering
- Vídeo: Influence Limiting and Attack Resistance; Interview with Paul Resnick
- Vídeo: Trust-Based Recommendation; Interview with Jen Golbeck
- Vídeo: Impact of Bad Ratings; Interview with Dan Cosley
SEMANA 2
User-User Collaborative Filtering Recommenders Part 2
2 vídeos, 2 leituras
- Vídeo: Assignment Introduction
- 阅读: Assignment Instructions: User-User CF
- 阅读: Introducing User-User CF Programming Assignment
- Vídeo: Programming Assignment - Programming User-User Collaborative Filtering
Nota atribuída: User-User CF Answer Sheet
Nota atribuída: User-User Collaborative Filtering Quiz
Nota atribuída: User-User CF Programming Assignment
SEMANA 3
Item-Item Collaborative Filtering Recommenders Part 1
6 vídeos
- Vídeo: Introduction to Item-Item Collaborative Filtering
- Vídeo: Item-Item Algorithm
- Vídeo: Item-Item on Unary Data
- Vídeo: Item-Item Hybrids and Extensions
- Vídeo: Strengths and Weaknesses of Item-Item Collaborative Filtering
- Vídeo: Interview with Brad Miller
SEMANA 4
Item-Item Collaborative Filtering Recommenders Part 2
2 vídeos, 2 leituras
- Vídeo: Item-Based CF Assignment Intro Video
- 阅读: Item-Based CF Assignment Instructions
- 阅读: Introducing Item-Item CF Programming Assignment
- Vídeo: Programming Assignment - Programming Item-Item Collaborative Filtering
Nota atribuída: Item Based Assignment Part l
Nota atribuída: Item Based Assignment Part II
Nota atribuída: Item Based Assignment Part III
Nota atribuída: Item Based Assignment Part IV
Nota atribuída: Item-Item CF Programming Assignment
Advanced Collaborative Filtering Topics
5 vídeos
- Vídeo: The Cold Start Problem
- Vídeo: Recommending for Groups: Interview with Anthony Jameson
- Vídeo: Threat Models
- Vídeo: Explanations
- Vídeo: Explanations, Part II: Interview with Nava Tintarev
Nota atribuída: Item-Based and Advanced Collaborative Filtering Topics Quiz
Perguntas frequentes
Como funciona
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
Desenvolvedores
Universidade de MinnesotaUniversidade de Minnesota
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
Custo
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Classificações e avaliações
All in all, it is a comprehensive introduction to collaborative filtering. It allows the reader which paradigms and what tools to use in specific situations. I still have some complains with the excel assignments though.
There is an error in the assignment week 4 : the spreadsheet normalize by user instead of by item
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
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The topics I am interested in covered by people who definitely has related expertise. But overall quality of the teaching materials expected to be higher. Forum is also a little bit deserted, although contains some critical hints to pass the assignments (such a hints worth to be included in the assignment descriptions itself). I want to support the course, and it is pity to give it only 4 of 5 stars, but I really expect more quality from the course I paid for.